Natural Logic is a deductive inference system that works directly on syntactic representations of natural language, with no intermediate translation to logical formulas. It thus side-steps problems involved in the translation of natural language to first-order logic, while being more precise than shallow inference techniques. In particular, Natural Logic focuses on monotonicity inference patterns, such as inferring "Nina has a dog" from "Nina has a bulldog" (i.e. replacing a specific term by a more general one in a positive context), or "Nina didn't get a rose" from "Nina didn't get a flower" (i.e. replacing a general term by a more specific one in a negative context).

In this talk I will present work in progress on implementing a multilingual Natural Logic engine that builds on Grammatical Framework and imports specificity hierarchies from an underlying ontology. The goal is to explore the applicability of Natural Logic to different inference patterns, and to investigate how language-independent those patterns are.

Short bio:
Christina Unger is a postdoctoral researcher in the Semantic Computing group affiliated to the Cluster of Excellence on Cognitive Interaction Technology (CITEC) at Bielefeld University. Her major research interest lies in the area of computational semantics, with a focus on ontology-based natural language understanding and question answering.